As smartphones, tablet personal computers (PCs), and other devices that can easily display a video have been widely used and a network environment to distribute videos has been fully improved, there have been an increasing number of opportunities to distribute video content in various forms. To distribute a video to various devices, the video is transcoded to forms corresponding to individual devices. Video quality may be lowered during the transcoding or distribution of the video, or the video may be destructed due to, for example, an error during video distribution. Accordingly, the video quality is checked before the video is displayed.
In one method of checking video quality, a person visually checks pieces of distributed content one by one. This method involves enormous human costs and burdens and imposes a physical limit when a large amount of content is checked. In view of this, there is a desire for technology that automatically evaluates video quality and thereby substantially reduces human tasks.
There are three types of methods of automatically evaluating video quality; a full reference (FR) method, in which all original videos before deterioration and all deteriorated videos are used, a reduced reference (RR) method, in which the features of two videos are compared, and a non-reference (NR) method, in which only deteriorated videos are used. The FR method enables quality to be highly precisely inferred because all information of a video is used, but is disadvantageous in that much processing time is taken.
The RR method enables quality to be inferred in less processing time than the FR method because the features of videos are compared. However, inference precision is lower than in the FR method accordingly. The NR method takes the least processing time among the three methods because only deteriorated videos are used for evaluation. However, it is generally said that inference precision in the NR method is the lowest among the three methods.
A conventional RR method will now be described. FIG. 15 illustrates the conventional RR method. In the example in FIG. 15, a feature creating unit 10a, a feature creating unit 10b, and a calculating unit 11 are included. The feature creating unit 10a creates a feature from an original video 1a and outputs the created feature to the calculating unit 11. The feature creating unit 10b creates a feature from a deteriorated video 1b and outputs the created feature to the calculating unit 11. The calculating unit 11 calculates a difference between the feature received from the feature creating unit 10a and the feature received from the feature creating unit 10b and outputs the calculation result as an evaluation value 1c. 
Next, a conventional technology that uses a RR method will be described. The conventional technology evaluates video quality by using the amount of edges in a video and changes in statistic S of image differences in the time direction. For example, the conventional technology obtains three evaluation values that represent the degree of an increase or a decrease in image edges, the degree of the strength of block noise, and the degree of image deterioration in the time direction.
FIG. 16 illustrates the conventional technology that uses a RR method. In the example in FIG. 16, a feature creating unit 30a, a feature creating unit 30b, and a calculating unit 30c are included. The feature creating unit 30a obtains of the distribution of a first feature, the distribution of a second feature, and the distribution of a third feature from the original video 1a. The feature creating unit 30a obtains statistics from the distributions of the first to third features and obtains a first deterioration feature, a second deterioration feature, and a third deterioration feature from the obtained statistics. The feature creating unit 30a then outputs the first to third deterioration features to the calculating unit 30c. 
The feature creating unit 30b obtains the distribution of the first feature, the distribution of the second feature, and the distribution of the third feature from the deteriorated video 1b. The feature creating unit 30b obtains statistics from the distributions of the first to third features and obtains a first deterioration feature, a second deterioration feature, and a third deterioration feature from the obtained statistics. The feature creating unit 30b then outputs the first to third deterioration features to the calculating unit 30c. 
The calculating unit 30c calculates an evaluation value 2a, an evaluation value 2b, and an evaluation value 2c from the first to third deterioration features received from the feature creating unit 30a and from the first to third deterioration features received from the feature creating unit 30b. Specifically, the calculating unit 30c calculates the evaluation value 2a from the first deterioration feature received from the feature creating unit 30a and from the first deterioration feature received from creating unit 30b; the calculating unit 30c calculates the evaluation value 2b from the second deterioration features received from the feature creating unit 30a and from the second deterioration feature received from creating unit 30b; and the calculating unit 30c calculates the evaluation value 2c from the third deterioration features received from the feature creating unit 30a and from the third deterioration feature received from creating unit 30b. For example, the evaluation value 2a represents the degree of an increase or a decrease in image edges, the evaluation value 2b represents the degree of the strength of block noise, and the evaluation value 2c represents the degree of image deterioration in the time direction.
The above technology is disclosed in, for example, Japanese Laid-open Patent Publication No. 6-133176, Japanese Laid-open Patent Publication No. 6-233013, International Publication Pamphlet No. WO 2009/133884, and Japanese Patent No. 2795147.